
import numpy as np
# 加载数据集
def loadDateSet():
    # 数据集中有5篇文档
    postingList=[['my', 'dog', 'has, flea', 'problems', 'help', 'please'],
                 ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
                 ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
                 ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
                 ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
                 ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']
                 ]
    # 标记上面的5篇文档，0为非侮辱类文档，1为侮辱类文档
    classVec = [0, 1, 0, 1, 0, 1]
    # 返回文档向量，和文档分类标记
    return postingList, classVec

# 创建一个去重列表
def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)

# 创建一个列表记录词袋中的词是否在文档中出现
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1
        else:
            print("%s 没在输入文档中出现" %word)
    return returnVec

# 朴素贝叶斯分类器训练函数
def trainNB0(trainMatrix, trainCategory):
    # 训练集中一共多少篇文档
    numTrainDocs = len(trainMatrix)
    # 获取词集的数量
    numWords = len(trainMatrix[0])
    # 计算侮辱性文档的概率
    pAbusive = sum(trainCategory) / float(numTrainDocs)
    # 分别初始化p0和p1的概率
    p0Num = np.zeros(numWords)
    p1Num = np.zeros(numWords)
    p0Denom = 0.0
    p1Denom = 0.0
    # 遍历训练集中的每一篇文档
    for i in range(numTrainDocs):
        # 如果是侮辱类的文档
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else: # 非侮辱类别
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = p1Num / p1Denom # 侮辱类别的
    p0Vect = p0Num / p0Denom
    return p0Vect, p1Vect, pAbusive

# 朴素贝叶斯分类函数
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)
    p0 = sum(vec2Classify * p0Vec) + np.log(1-pClass1)
    if p1 > p0:
        return 1
    else:
        return 0

# 测试代码
def testingNB():
    listOPosts, listClasses = loadDateSet()
    myVocabList = createVocabList(listOPosts)
    trainMat = []
    for postInDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postInDoc))
    p0V, p1V, pAbuse = trainNB0(np.array(trainMat), np.array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as:', classifyNB(thisDoc, p0V, p1V, pAbuse))
    testEntry = ['stupid', 'garbage']
    thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as:', classifyNB(thisDoc, p0V, p1V, pAbuse))

def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def textParse(bigString):    #input is big string, #output is word list
    import re
    listOfTokens = re.split(r'\W*', bigString)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def spamTest():
    docList=[]; classList = []; fullText =[]
    for i in range(1,26):
        wordList = textParse(open('email/spam/%d.txt' % i ,encoding='gb18030', errors='ignore').read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('email/ham/%d.txt' % i ,encoding='gb18030', errors='ignore').read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    trainingSet = list(range(50))
    testSet=[]           #create test set
    for i in range(10):
        randIndex = int(np.random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat=[]
    trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V,p1V,pSpam = trainNB0(np.array(trainMat), np.array(trainClasses))
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(np.array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
            errorCount += 1
            print ("classification error", docList[docIndex])
    print ('the error rate is: ', float(errorCount)/len(testSet))
    #return vocabList,fullText